similar to: relaimpo - dummy variable

I'm having trouble getting the relaimpo package to use a covariance matrix as input. I'm getting an error message that reads as follows: Error in eval(m$weights, data, parent.frame()) : numeric 'envir' arg not of length one I'm guessing there is something wrong with the structure of my covariance matrix, but it looks fine to me. Pardon my R ignorance if this is an easy

I am currently using the relaimpo package to estimate the relative importance of regressors (N= 4000): > m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data) > calc.relimp(m1, rela=TRUE) > m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg") > booteval.relimp(m2) > plot(booteval.relimp(m2)) In a new dataset with 3 measurement points (0,6,12 weeks), I want to perform a similar

Dear developeRs, with R 2.4.1 (and also 2.4.0), the function str() fails on objects of class relimplmbooteval, if there are unused slots, which is very often the case. I am not sure whether this is a bug in str() or a correct behavior of str() that unmasks some sloppiness in my usage of S4 classes (that I am not aware of)? Reproducible example (package relaimpo needed): The program

Hello! I am trying test my observed data against the null-hypothesis that different items from a psychological questionnaire contribute equally to the metric dependent variable that measures problems (sum score of a questionnaire). That is, I am interested in relative strength of the predictors. Predictor items of the questionnaire are on a scale from 0-3, and technically ordinal, although most

Dears, Unfortunatelly, the packages relaimpo and relimp do not seem to work with plm function (plm package) or gls function (in nlm package). I've been studying on how to adapt one of them for this pourpose. In that sense, I have two questions regarding to this work: 1) have anyone hard of any workaround for those incompatibilities, or at least of any ideas on that - especially for plm? 2)

Hello, I'm new to boostrapping and I'd need some help to understand the error message that pops up when I run my script. I have a data.frame with 73 lines and 21 column. I am running a stepwise regression to find the best model using the R function "step". I apply bootstrapping to obtain model coefficients. This is my script: # "datare80" is the name of the

Hello, Does anyone know if relaimpo only applies to "pure" multiple linear regression models, i.e. - linear in the variables AND linear in the coefficients or is it safe to use it in models that are: - non-linear in the variables BUT linear in the coefficients? Thanks Jyotin [[alternative HTML version deleted]]

Dear userRs, Version 2.0 of package relaimpo is on CRAN (and on my homepage with an additional metric for non-US users). It contains several major improvements: - usage of factors - incorporation of interactions (for metric lmg only) - incorporation of observation weights - application to data from complex surveys - processing of multiply imputed data The latter two points are somewhat

Dear all, I am stuck with tracking down an error I get from R CMD check (Windows, R 2.4.0, same error for R 2.2.1): when running R CMD check over a modified version of package relaimpo, I get the following output in install.out: preparing package relaimpo for lazy loading Loading required package: MASS Loading required package: boot Error in parse(file, n, text, prompt) : syntax error at 2193:

Dear R-developers, I'm experiencing a problem with having an example run under R 2.6.1 RC (downloaded and installed today, r43513), which does not occur on R 2.6.0: The new version of package relaimpo does list package survey under "Depends", and an example directly (i.e. not only from within a function of relaimpo) uses function svydesign from package survey. Package survey is

I have installed the latest relaimpo library ( form their website with the 8 functions) When running pvmd as a type in c=type("pmvd") in calc.relimp I get the error message ...could not find function "pmvdcalc" (this is in R version 2.12.1) Can anyone help? Paul Prof P Rheeder School of Health Systems and Public Health Faculty of Health Sciences University of Pretoria Room

Dear All: I am calculating the relative importance of a regressor in a linear model. Does anyone know how I can obtain/install the 'pmvd' computation type? I am a US user. Regards, Y -- View this message in context: http://r.789695.n4.nabble.com/calc-relimp-pmvd-for-US-R-user-tp3808752p3808752.html Sent from the R help mailing list archive at Nabble.com.

HAVE_SENDMSG = 1 HAVE_ACCRIGHTS_IN_MSGHDR - not defined HAVE_CONTROL_IN_MSGHDR = 1 > void > mm_send_fd(int socket, int fd) > { > #if defined(HAVE_SENDMSG) && (defined(HAVE_ACCRIGHTS_IN_MSGHDR) || defined(HAVE_CONTROL_IN_MSGHDR)) > struct msghdr msg; > struct iovec vec; > char ch = '\0'; > int n; > #ifndef HAVE_ACCRIGHTS_IN_MSGHDR

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

1) Packages to be used- For smaller datasets use these 1. CAR Package http://cran.r-project.org/web/packages/car/index.html 2. GVLMA Package http://cran.r-project.org/web/packages/gvlma/index.html 3. ROCR Package http://rocr.bioinf.mpi-sb.mpg.de/ 4. Relaimpo Package 5. DAAG package 6. MASS package 7. Bootstrap package 8. Leaps package Also see

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working

Hi, Attached is a patch I'm testing. It changes how "MachineFrameInfo::HasCalls" is calculated. Basically, the way it's calculated now is in PEI. And it only looks for frame adjustments (or inline ASM) to determine if the function has a call or not. This way is much more accurate and occurs much sooner. But it breaks this test:

Hi R-Users, I have a student doing work with lionfish and she has been trying to analyse a multivariate dataset to see what variables/factors are influencing the behaviour of lionfish. We have attempted a number of analyses, including rpart, relimpo and standard linear regression but we are not having much luck with quality output. The data is very non-normal and we would appreciate some advice

When doing cpu hotplug in a KVM guest with virtio blk I get warnings like 747.652408] ------------[ cut here ]------------ [ 747.652410] WARNING: CPU: 4 PID: 2895 at block/blk-mq.c:1144 __blk_mq_run_hw_queue+0xd4/0x100 [ 747.652410] Modules linked in: dm_multipath [ 747.652412] CPU: 4 PID: 2895 Comm: kworker/4:1H Tainted: G W 4.14.0+ #191 [ 747.652412] Hardware name: IBM 2964

When doing cpu hotplug in a KVM guest with virtio blk I get warnings like 747.652408] ------------[ cut here ]------------ [ 747.652410] WARNING: CPU: 4 PID: 2895 at block/blk-mq.c:1144 __blk_mq_run_hw_queue+0xd4/0x100 [ 747.652410] Modules linked in: dm_multipath [ 747.652412] CPU: 4 PID: 2895 Comm: kworker/4:1H Tainted: G W 4.14.0+ #191 [ 747.652412] Hardware name: IBM 2964